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Cross-Platform Testing with AI/ML Benefits & Challenges

Drivers for cross-platform testing

Accelerated adoption of digital has impaired users when it comes to the choice of device, operating system, browser as well as a platform to access any application. The defining pillar of customer experience in this digital landscape is a seamless experience across multiple channels, each with its device – Operating System (OS) combination, which is possible through cross-platform testing.

Cross-platform testing helps uncover application issues when ported across any combination of devices and operating systems. It also addresses a wide range of areas including but not limited to testing for usability, consistency, performance, and experience across a multitude of device platform combinations.

According to StatCounter Mobile Operating System Market Share Worldwide – August 2022, 71.5% market share rests with Android, 27.81% with iOS, and remaining across others like Samsung, KaiOS, Windows, etc. Operating systems like Android further have multiple versions ranging from 7 to 11. In addition, there exists a wide range of devices and their model variants, as well as OS minor releases, patches, and code fixes. All these, in combination with the scale factor, make cross-platform testing key to ensuring superior customer experiences.

Selecting the right cross-platform test tool stack

In cross-platform testing, test strategy, test scenarios, and test cases remain the same irrespective of the device OS combination. The key to success depends to a large extent on the same test suite being run across multiple device combinations at a faster pace with shorter feedback loops.

Automation with the right tool suite is paramount to achieving this objective. The primary functionality that needs to be checked while selecting a tool is the ability to support as many device platform combinations as possible. The wider the coverage, the more effective the quality assurance of the application under test. Such tools should also have the ability to update themselves with newer versions of the platforms constantly. Leading tools in this space, like Lambdatest, Smartbear, and Browserstack, provide the ability to test across 2000+ device-OS-browser combinations.

Equally important is the ability of the tool to provide detailed analytical reports with the capability to drill down, provide real-time feedback, and provide detailed test reports. These enable the developer to understand which application components are susceptible to failure and in which device platform combination.

Testing on real devices is ideal but may not be possible when it comes to various combinations. Furthermore, setting up real-life environments can prove to be costly. Hence the tool should have the ability to provide simulations of real-life scenarios. Tools like Browser Stack, Lambdatest, and SmartBear enable such functionalities. Testing across simulated environments augments automation and regression tests, thereby speeding up quality checks considerably.

The need for going to market faster with multiple application releases has increased the adoption of CI/CD principles for application development. A cross-platform testing tool needs to be able to incorporate CI/CD and DevOps principles. In addition, having the ability to run parallel tests across multiple environments is a strong positive since it also enables faster feedback loops in CI/CD-led development cycles.

Finally, selecting a cloud-based tool provides scalability, accessibility, and flexibility. It also gives the ability to execute parallel testing – multiple teams across locations can test the application simultaneously while driving collaboration across teams. The cloud-based tool also provides ready access to various environments with varying combinations.

The role of AI in cross-platform testing

The traditional model of cross-platform testing hinges on the existing testing framework, whereby similar test scenarios are run across multiple device-OS-browser combinations. These combinations are specified by the application developer based on their understanding of all platforms that can be used by the end user to access the application.

However, the modern application architecture that is more connected to new-age systems like chatbots and sophisticated omnichannel applications requires an approach that incorporates AI principles and frameworks. On the one hand, these provide the ability to drive parallel testing using containers, deliver screenshots of test results across each device-OS combination, and on the other, help to drive analysis through visual AI techniques whereby results screen can be compared with existing baselines to witness changes.

AI platforms bring forth the ability to analyze test scripts in real-time and make changes based on any errors that are uncovered. They are capable enough to capture how the audience navigates on real devices and make course corrections both in terms of identifying the scenarios that create maximum impact on the application under test and account for maximum risk when it comes to loss of application functionality or experience.

Furthermore, AI platforms also provide the ability to call out the device–platform combinations that are less frequently in use. This provides the ability to streamline the test cases for optimum utilization by prioritizing the combinations based on heavy usage. It’s enough for the least used combinations to have minimal/no variation of test cases than what has been created for higher-use combinations.

The availability of large volumes of device-dependent test data is crucial when it comes to cross-platform testing. Test data that originates from a wide range of sources need to be analyzed to arrive at the ones that are meaningful for the test scenarios. Adopting AI/ML frameworks can also play a key role in helping to sort through the data, analyze it and map meaningfully to create the right test data sets.

Challenges of AI-powered testing

While powering cross-platform testing with AI tools and frameworks is game-changing, challenges abound when it comes to AI adoption. The lack of experts with thorough knowledge on adopting and maximizing benefits from AI tool stacks is of critical importance. Such platforms being nascent, also suffer from integration issues, especially when it comes to existing technology environments that are legacy based.

Lack of clarity on a long-term strategy for AI adoption and analyzing ROI from AI-based engagements further add to the dilemma. Being an evolving field, security concerns also abound for AI-based approaches. Many tools like Selenium have AI-powered versions but need work when it comes to building trust in the minds of the users. This further hinders the adoption of such AI-backed tools.

In conclusion, cross-platform testing ably backed by automation tool suites is the right approach to drive seamless and superior customer experiences. While there exists a wide range of tool stacks that help to achieve this objective, paramount before selecting any tool is the need to have a clear understanding of available devices/platforms/OS, their respective versions, and the missing combinations that need to be addressed by means of simulators.

AI-backed approaches, as well as tools and frameworks, bring to the table the ability to drive cross-platform testing more intelligently and at a much faster pace. However, such approaches need to be taken up with due diligence, given the maturity level of adoption of these platforms.

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